We have used the scBALF-COVID-19 dataset prepared for the single-cell transcriptomics challenge of the IEEE COVID-19 Data Hackathon. This dataset is derived from public data [1]} and contains a set of Broncho Alveolar Lavage Fluid (BALF) cells from patients categorized clinically as having mild, severe or no COVID-19 infection. In detail, the dataset has scRNA-seq data of 23189 BALF cells from three classes based on COVID-19 severity: mild infection (3292 cells), severe infection (7919 cells), and no infection (11978 cells) across 1999 genes. The dataset has been normalized for technical differences between patients (batch normalization) as well as sequencing depth differences between cells in each patient. This normalization step, has removed the overdispersion inherent in typical scRNA-seq data. As a result, it may not be beneficial to apply typical scRNA-seq tools for biomarker identification and cell clustering on this normalized data set, such as scVI [2]}, DESeq2 [3]} and SimCD [4]}, in which Negative Binomial (NB) distribution models and their extensions were developed to model scRNA-seq data. As shown in the histogram plot of normalized gene expression values for a random gene “CXCR1” in the left plot of Fig REF , normalized gene expression profiles across samples can be approximated with a normal distribution.
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